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Record W2942302541

Why Do Backpackers Come to New Zealand?: A Blend of Motivating Factors

2011· article· en· W2942302541 on OpenAlexaboutno aff
Jane Godfrey

Bibliographic record

VenueCAUTHE 2011: National Conference: Tourism : Creating a Brilliant Blend · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsAdvertisingGeographySociologyPublic relationsPolitical scienceBusiness
DOInot available

Abstract

fetched live from OpenAlex

This paper explores the motivations of backpackers and the reasons they give for leaving home and for choosing New Zealand specifically as a travel destination. It is based on research carried out in Queenstown in New Zealand which involved semi-structured in-depth interviews with fourteen backpackers. Those interviewed were all aged between nineteen and thirty‐four years old and came from, Ireland, and Canada. Backpackers were questioned on their motivations for travel and details of their travel history and current travel behaviour. This paper explores some of the motivations that emerged from those interviews. The main reason given for leaving home was the desire to . main reason given for choosing New Zealand as a destination was its scenery. For several, there was no specific reason for choosing New Zealand, so much as it was perceived as a must-see stop on the backpacker route. For most of those interviewed, the decision to New Zealand was not due to a single motivating factor but instead a 'blend' of different factors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.098
GPT teacher head0.331
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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Same venueCAUTHE 2011: National Conference: Tourism : Creating a Brilliant BlendSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207